Tamr Launches Fall 2019 Data Unification System to Power Breakthrough Analytic Insights
Latest Release Supports AWS, Google Cloud and Microsoft Azure; Adds Geospatial Mapping and More to Help Enterprises Unify and Clean Dirty Data
Tamr, Inc. announced the general availability of the Fall 2019 release of the company’s patented data unification system. Tamr is a data integration software platform that uses supervised machine learning to unify data silos. It enables data engineers (who aren’t data scientists) to harness the power of machine learning to build automated pipelines that integrate, master, and classify disparate, dirty data. With the Fall 2019 release, Tamr offers cloud support, geospatial mapping and additional enhancements to make it faster and easier to unify large numbers of heterogeneous sources.
A unifying force for the cloud
The adoption of cloud computing continues at a phenomenal pace and is fast becoming the default at companies of all sizes. With a growing number of high-profile companies implementing cloud strategies and most organizations using at least some form of cloud services, organizations will continue to accelerate migration of their computing environments to the cloud and at an accelerated rate. Tamr Fall 2019 offers support and capability for Amazon Web Services, Google Cloud and Microsoft Azure and provides a flexible and powerful way for users to find, gather, unify and format data from many disparate sources.
“Tamr’s speed and efficiency in conjunction with AWS’s industry-leading scalability and security has allowed TME to benefit from machine learning in the Tamr data unification platform,” said Filip Salaets, IT manager customer & retailer data with Toyota Motor Europe. “As a result, TME has seen a 40% reduction of duplicative customer records allowing for an increase in efficiency and business value.”
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Leading the charge in geospatial data
Historically, it was nearly impossible for a large enterprise to optimize the use of geospatial data – specifically in relation to other datasets and attributes. Now, with Tamr’s new geospatial data capability, users can:
- Match pairs of records that contain geospatial data,
- Run transformations on records with geospatial data, such as calculate the area or the perimeter.
- Put matched records into clusters based on features extracted from geospatial data and eliminate duplicates, and
- Align records containing geospatial data with existing taxonomies.
Additional Tamr Fall 2019 features include
- Access Control
- Tamr now offers a policy-based access control, where you may apply security policies to users, groups, and resources to govern their read and write access in Tamr. Migration to the new access control scheme is automatic as part of this version’s installation process.
- Job Management
- Tamr users can run jobs in parallel and configure the system to have sufficient resources for running multiple jobs concurrently. When sharing the same instance with others in a group, the administrator can configure Tamr to enable running jobs in parallel.
- Steward
- Steward is a new approach to collecting feedback on analytics. It is the first feedback system built for analytics. Tamr users can collect context-rich issues in a single place, collaboratively resolve them, and increase analytic usage.
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Tamr’s innovative approach that leverages the third generation of AI, machine learning, to unify myriad diverse data sources has attracted many large customers including Thomson Reuters, Samsung, GE, and Toyota and also received recent acclaim as a member of Forbes’ inaugural AI 50 list.
“We’re committed to helping large enterprises solve the data unification challenge, and the Fall 2019 release advances our leadership position in the marketplace,” said Mark Marinelli, Tamr’s head of product management. “By bringing comprehensive, scalable data unification to the cloud and integrating geospatial site data within the overall agile data mastering process, enterprises can understand and use high-quality, unified data to generate accurate, trustworthy business insights.”
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